In data analytics, a “tag” may be a small piece of code that is insertable or embeddable in a code base of a program. The tag may be configured to collect various types of information from the tagged source. Tags applied to a website, for example, may generate performance data associated with that website, such as the number of visitors in a timeframe, popular web pages, number of transactions, revenue-based trends, etc.
For software developers and other participants in user-interface/user-experience (UI/UX) design processes (e.g., product managers, designers, analysts), however, tagging may not be a seamless experience. Tagging numerous UI screens, for example, may be a tedious and time-consuming process, which may require a developer to manually tag all relevant components of each UI screen depending on the overall design of the UX. One typical problem arising out of this inherently manual process of tagging is that when the UI/UX design (e.g., sequence of the UI screens) is in any way altered, the developer must also manually modify the relevant tags to match the alteration. For example, if the third screen in a flow sequence is moved up to be the first screen, all tags previously associated with the third screen must now be modified to be associated with the first screen (in addition to the modifications of all effects to other screens in the existing service flow) so that data collected by those tags are correctly categorized and analyzed.
Moreover, the tagging process becomes highly complex when digital experiences have interconnected parts, complex features with distinct points of entry, or have multiple sets of users with different sets of rules. In addition, the process from UI/UX design to actually implementing the tags into the codebase may be disjointed, the extent of which is difficult to know until some level of testing is performed. Thus, there is a need for optimizing various aspects of the tagging and UI/UX design processes.